This article presents the Asymmetric Cross-Market Dynamics Network (ACMD-Net), a forecasting framework built on the premise that commodity markets are fundamentally asymmetric. Three symmetry assumptions are statistically tested and rejected: volatility symmetry via the GJR-GARCH leverage test $\gamma >0$, $p<0.001$, coupling symmetry via the directional Granger causality DM statistic, 3.18–3.67, $p<0.001$, and cointegration symmetry via a likelihood ratio test, $p<0.01$. Each rejected hypothesis motivates a corresponding architectural component, yielding causally interpretable forecasts unavailable in black-box alternatives. The model is evaluated on daily CBOT futures for corn, wheat, and soybeans from January 2010 to December 2023, $T=3508$. ACMD-Net achieves RMSE reductions of 37–42% over ARIMA and 15–17% over standard LSTM. At short horizons ($h=1$), TFT achieves marginally lower point RMSE (3–4%, not statistically significant; DM $p>0.05$); at long horizons ($h=22$), TFT continues to report the lowest point RMSE across all commodities; differences versus ACMD-Net are not statistically significant DM $<1.96$ for all commodities. The architecture’s predictive value lies in economically grounded interpretability and superior directional accuracy rather than universal RMSE dominance. Directional accuracy ranges from 60 to 62% $p<0.001$, and net-positive trading returns are obtained for wheat and soybeans at 8–12-basis-point transaction costs. Ablation analysis identifies temporal attention as the primary performance driver, RMSE $+22.2\%$, upon removal, with econometric features contributing an additional 24.9% gain.